Deep-Reinforcement-Learning-Algorithms
tianshou
| Deep-Reinforcement-Learning-Algorithms | tianshou | |
|---|---|---|
| 3 | 8 | |
| 580 | 7,435 | |
| - | 1.7% | |
| 3.6 | 9.5 | |
| almost 3 years ago | 2 days ago | |
| Jupyter Notebook | Python | |
| - | MIT License |
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Deep-Reinforcement-Learning-Algorithms
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Is there a canonical simple "helloworld" neural network design? Something beyond AND/OR logic, a handful of nodes that does something mildly "useful"?
I guess the most spectacular in terms of performance/"brain size" ratio is a 2 neuron, 8 weights network https://github.com/Rafael1s/Deep-Reinforcement-Learning-Algorithms/tree/master/CartPole-Policy-Based-Hill-Climbing
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Training time of CartPole is way to long
It can be solved in 113 episodes by Hill Climbing algorithm, https://github.com/Rafael1s/Deep-Reinforcement-Learning-Algorithms/tree/master/CartPole-Policy-Based-Hill-Climbingor by Double Deep Q-Learning in 612 episodes, https://github.com/Rafael1s/Deep-Reinforcement-Learning-Algorithms/tree/master/Cartpole-Double-Deep-Q-Learning
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Need help with PyTorch script for Actor_Critic implementation of MountainCar env.
You can find the solution for MountainCar env here: https://github.com/Rafael1s/Deep-Reinforcement-Learning-Algorithms/tree/master/MountainCarContinuous-TD3This solution implemented using PyTorch. The TD3 model is the successor to DDPG algorithm using the Actor-Critic model.
tianshou
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Is it better to not use the Target Update Frequency in Double DQN or depends on the application?
The tianshou implementation I found at https://github.com/thu-ml/tianshou/blob/master/tianshou/policy/modelfree/dqn.py is DQN by default.
- 他們能回來嗎
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Multi-Agent Stable Baselines
https://github.com/thu-ml/tianshou Imho there isn't a library that has it all, RLlib is quite good too, but I think that Tianshou is more similar to Pytorch and that helps to change the internals more intuitively and know what you are doing.
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Question about the old policy and new policy in TRPO code
Good point...I'll check in more detail when I get a chance later today! I would suggest looking at a more recent implementation like https://github.com/DLR-RM/stable-baselines3 or https://github.com/thu-ml/tianshou if you're trying to build. https://spinningup.openai.com/en/latest/algorithms/trpo.html is particularly good for understanding
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Tensorflow vs PyTorch for A3C
Do you absolutely need A3C? A2C has become more widely used (see, e.g., the comment in https://github.com/ikostrikov/pytorch-a3c, and the fact that both https://github.com/thu-ml/tianshou and https://github.com/facebookresearch/salina have A2C implementations, but no A3C at first glance).
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"Tianshou: a Highly Modularized Deep Reinforcement Learning Library", Weng et al 2021 (Python PyTorch MuJuCo; PPO, DQN, A2C, DDPG, SAC, TD3, REINFORCE, NPG, TRPO, ACKTR)
Code for https://arxiv.org/abs/2107.14171 found: https://github.com/thu-ml/tianshou/
Get the code for Tianshou here (GitHub).
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Best PyTorch RL library for doing research
I tried tianshou and thought it was well-designed for modularity, but it was early in development when I tried and missing some basic features
What are some alternatives?
ElegantRL - Massively Parallel Deep Reinforcement Learning. 🔥
stable-baselines3 - PyTorch version of Stable Baselines, reliable implementations of reinforcement learning algorithms.
Deep-Reinforcement-Learning-for-Automated-Stock-Trading-Ensemble-Strategy-ICAIF-2020 - Deep Reinforcement Learning for Automated Stock Trading: An Ensemble Strategy. ICAIF 2020. Please star. [Moved to: https://github.com/AI4Finance-Foundation/Deep-Reinforcement-Learning-for-Automated-Stock-Trading-Ensemble-Strategy-ICAIF-2020]
cleanrl - High-quality single file implementation of Deep Reinforcement Learning algorithms with research-friendly features (PPO, DQN, C51, DDPG, TD3, SAC, PPG)
Popular-RL-Algorithms - PyTorch implementation of Soft Actor-Critic (SAC), Twin Delayed DDPG (TD3), Actor-Critic (AC/A2C), Proximal Policy Optimization (PPO), QT-Opt, PointNet..
DeepRL-TensorFlow2 - 🐋 Simple implementations of various popular Deep Reinforcement Learning algorithms using TensorFlow2
pytorch-learn-reinforcement-learning - A collection of various RL algorithms like policy gradients, DQN and PPO. The goal of this repo will be to make it a go-to resource for learning about RL. How to visualize, debug and solve RL problems. I've additionally included playground.py for learning more about OpenAI gym, etc.
rl_lib - Series of deep reinforcement learning algorithms 🤖
seed_rl - SEED RL: Scalable and Efficient Deep-RL with Accelerated Central Inference. Implements IMPALA and R2D2 algorithms in TF2 with SEED's architecture.
pytorch-a3c - PyTorch implementation of Asynchronous Advantage Actor Critic (A3C) from "Asynchronous Methods for Deep Reinforcement Learning".
Deep-Reinforcement-Learning-Algorithms-with-PyTorch - PyTorch implementations of deep reinforcement learning algorithms and environments